Domain-Adversarial Training of Neural Networks

Abstract

We introduce a new representation learning approach for
domain adaptation, in which data at training and test time come
from similar but different distributions. Our approach is
directly inspired by the theory on domain adaptation suggesting
that, for effective domain transfer to be achieved, predictions
must be made based on features that cannot discriminate between
the training (source) and test (target) domains.

The
approach implements this idea in the context of neural network
architectures that are trained on labeled data from the source
domain and unlabeled data from the target domain (no labeled
target-domain data is necessary). As the training progresses,
the approach promotes the emergence of features that are (i)
discriminative for the main learning task on the source domain
and (ii) indiscriminate with respect to the shift between the
domains. We show that this adaptation behaviour can be achieved
in almost any feed-forward model by augmenting it with few
standard layers and a new gradient reversal layer. The
resulting augmented architecture can be trained using standard
backpropagation and stochastic gradient descent, and can thus be
implemented with little effort using any of the deep learning
packages.

We demonstrate the success of our approach for
two distinct classification problems (document sentiment
analysis and image classification), where state-of-the-art
domain adaptation performance on standard benchmarks is
achieved. We also validate the approach for descriptor learning
task in the context of person re-identification application.